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janusflow-workflows

janusflow-workflows

Guides JanusFlow understanding and rectified-flow image generation with diffusers and an SDXL VAE.

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name: janusflow-workflows description: "Guides JanusFlow understanding and rectified-flow image generation with diffusers and an SDXL VAE." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

JanusFlow Workflows

Use this sub-skill when the task mentions JanusFlow, rectified flow, SDXL VAE, image_gen_tag, or the JanusFlow-1.3B model family.

Read first

For installation and model-family selection, use the root ../../references/installation-and-models.md.

When to use this route

Choose this route when the user asks to:

  • Generate images with JanusFlow-1.3B.
  • Debug the rectified-flow loop, image_gen_tag, or VAE decode path.
  • Adapt the JanusFlow demo.
  • Compare JanusFlow with Janus / Janus-Pro understanding or generation.
  • Investigate diffusers compatibility with the repository's torch wheel.

Core workflow

  1. Load the JanusFlow processor and model.
  2. Load the SDXL VAE through diffusers.
  3. Build a one-turn conversation and append image_gen_tag.
  4. Tokenize the prompt and remove the last generation token before the flow loop.
  5. Sample a latent z tensor and run the ODE-style update loop.
  6. Decode the final latent through the vision decoder and VAE.
  7. Save the resulting images.

Dry-run before model execution

Validate the prompt and generation parameters without downloading weights:

python sub-skills/janusflow-workflows/scripts/janusflow_text_to_image.py \
  --model-id deepseek-ai/JanusFlow-1.3B \
  --prompt "A glass of red wine on a reflective surface."

The default mode prints a plan and does not download model weights. Add --run-model only after you confirm diffusers compatibility, CUDA, and SDXL VAE access.

Route elsewhere

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